Recent studies have demonstrated that smart grids are vulnerable to stealthy
false data injection attacks (SFDIAs), as SFDIAs can bypass residual-based bad
data detection mechanisms. The SFDIA detection has become one of the focuses of
smart grid research. Methods based on deep learning technology have shown
promising accuracy in the detection of SFDIAs. However, most existing methods
rely on the temporal structure of a sequence of measurements but do not take
account of the spatial structure between buses and transmission lines. To
address this issue, we propose a spatiotemporal deep network, PowerFDNet, for
the SFDIA detection in AC-model power grids. The PowerFDNet consists of two
sub-architectures: spatial architecture (SA) and temporal architecture (TA).
The SA is aimed at extracting representations of bus/line measurements and
modeling the spatial structure based on their representations. The TA is aimed
at modeling the temporal structure of a sequence of measurements. Therefore,
the proposed PowerFDNet can effectively model the spatiotemporal structure of
measurements. Case studies on the detection of SFDIAs on the benchmark smart
grids show that the PowerFDNet achieved significant improvement compared with
the state-of-the-art SFDIA detection methods. In addition, an IoT-oriented
lightweight prototype of size 52 MB is implemented and tested for mobile
devices, which demonstrates the potential applications on mobile devices. The
trained model will be available at
\textit{this https URL}.

本文提出了一种基于深度学习技术的时空深度网络 PowerFDNet，用于 AC 模型电网中 SFDIA 检测，PowerFDNet 可以有效地建模测量的时空结构，并在基准智能电网上的实验结果证明了其性能的显著改善，同时作者们还实现了一个 IoT 导向的轻型原型，证明了它在移动设备上的潜在应用。

PowerFDNet：基于深度学习的交流模型输电系统隐蔽虚假数据注入攻击检测

PowerFDNet: Deep Learning-Based Stealthy False Data Injection Attack Detection for AC-model Transmission Systems

State estimation is of considerable significance for the power system
operation and control. However, well-designed false data injection attacks can
utilize blind spots in conventional residual-based bad data detection methods
to manipulate measurements in a coordinated manner and thus affect the secure
operation and economic dispatch of grids. In this paper, we propose a detection
approach based on an autoencoder neural network. By training the network on the
dependencies intrinsic in 'normal' operation data, it effectively overcomes the
challenge of unbalanced training data that is inherent in power system attack
detection. To evaluate the detection performance of the proposed mechanism, we
conduct a series of experiments on the IEEE 118-bus power system. The
experiments demonstrate that the proposed autoencoder detector displays robust
detection performance under a variety of attack scenarios.

本文提出了一种基于自动编码器神经网络的检测方法，通过训练数据依赖性有效地克服了电力系统攻击检测中固有的训练数据不平衡性，实现了对假数据注入攻击的检测，并在 IEEE 118 节点电力系统上进行了实验验证性能表现良好。

利用自编码器方法检测虚假数据注入攻击

Detection of False Data Injection Attacks Using the Autoencoder Approach

Modern advances in sensor, computing, and communication technologies enable
various smart grid applications. The heavy dependence on communication
technology has highlighted the vulnerability of the electricity grid to false
data injection (FDI) attacks that can bypass bad data detection mechanisms.
Existing mitigation in the power system either focus on redundant measurements
or protect a set of basic measurements. These methods make specific assumptions
about FDI attacks, which are often restrictive and inadequate to deal with
modern cyber threats. In the proposed approach, a deep learning based framework
is used to detect injected data measurement. Our time-series anomaly detector
adopts a Convolutional Neural Network (CNN) and a Long Short Term Memory (LSTM)
network. To effectively estimate system variables, our approach observes both
data measurements and network level features to jointly learn system states.
The proposed system is tested on IEEE 39-bus system. Experimental analysis
shows that the deep learning algorithm can identify anomalies which cannot be
detected by traditional state estimation bad data detection.

研究使用深度学习技术检测注入数据度量值的方法，运用卷积神经网络与长短时记忆网络，观察数据测量和网络水平特征以共同学习系统状态，以此有效估算系统变量，实验结果表明，这种深度学习算法可以识别传统状态估计坏数据检测无法检测到的异常。